Dynamic joint sentiment-topic model

Social media data are produced continuously by a large and uncontrolled number of users. The dynamic nature of such data requires the sentiment and topic analysis model to be also dynamically updated, capturing the most recent language use of sentiments and topics in text. We propose a dynamic Joint Sentiment-Topic model (dJST) which allows the detection and tracking of views of current and recurrent interests and shifts in topic and sentiment. Both topic and sentiment dynamics are captured by assuming that the current sentiment-topic-specific word distributions are generated according to the word distributions at previous epochs. We study three different ways of accounting for such dependency information: (1) Sliding window where the current sentiment-topic word distributions are dependent on the previous sentiment-topic-specific word distributions in the last S epochs; (2) skip model where history sentiment topic word distributions are considered by skipping some epochs in between; and (3) multiscale model where previous long- and shorttimescale distributions are taken into consideration. We derive efficient online inference procedures to sequentially update the model with newly arrived data and show the effectiveness of our proposed model on the Mozilla add-on reviews crawled between 2007 and 2011. © 2013 ACM 2157-6904/2013/12-ART5 $ 15.00.

Joint sentiment/topic model for sentiment analysis

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA), called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text. Unlike other machine learning approaches to sentiment classification which often require labeled corpora for classifier training, the proposed JST model is fully unsupervised. The model has been evaluated on the movie review dataset to classify the review sentiment polarity and minimum prior information have also been explored to further improve the sentiment classification accuracy. Preliminary experiments have shown promising results achieved by JST.

Weakly supervised joint sentiment-topic detection from text

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework called joint sentiment-topic (JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and topic simultaneously from text. A reparameterized version of the JST model called Reverse-JST, obtained by reversing the sequence of sentiment and topic generation in the modeling process, is also studied. Although JST is equivalent to Reverse-JST without a hierarchical prior, extensive experiments show that when sentiment priors are added, JST performs consistently better than Reverse-JST. Besides, unlike supervised approaches to sentiment classification which often fail to produce satisfactory performance when shifting to other domains, the weakly supervised nature of JST makes it highly portable to other domains. This is verified by the experimental results on data sets from five different domains where the JST model even outperforms existing semi-supervised approaches in some of the data sets despite using no labeled documents. Moreover, the topics and topic sentiment detected by JST are indeed coherent and informative. We hypothesize that the JST model can readily meet the demand of large-scale sentiment analysis from the web in an open-ended fashion.

Semantic sentiment analysis of Twitter

Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. Apple product) as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification.

Safety and health in mines: the rise of bureaucracy in the coal mining industry and its results as evidenced by the public enquiry into an accident at Houghton Main Colliery

This thesis is concerned with certain aspects of the Public Inquiry into the accident at Houghton Main Colliery in June 1975. It examines whether prior to the accident there existed at the Colliery a situation in which too much reliance was being placed upon state regulation and too Iittle upon personal responsibility. I study the phenomenon of state regulation. This is done (a) by analysis of selected writings on state regulation/intervention/interference/bureaucracy (the words are used synonymously) over the last two hundred years, specifically those of Marx on the 1866 Committee on Mines, and (b) by studying Chadwick and Tremenheere, leading and contrasting "bureaucrats" of the mid-nineteenth century. The bureaucratisation of the mining industry over the period 1835-1954 is described, and it is demonstrated that the industry obtained and now possesses those characteristics outlined by Max Weber in his model of bureaucracy. I analyse criticisms of the model and find them to be relevant, in that they facilitate understanding both of the circumstances of the accident and of the Inquiry . Further understanding of the circumstances and causes of the accident was gained by attendance at the lnquiry and by interviewing many of those involved in the Inquiry. I analyse many aspects of the Inquiry - its objectives. structure, procedure and conflicting interests - and find that, although the Inquiry had many of the symbols of bureaucracy, it suffered not from " too much" outside interference. but rather from the coal mining industry's shared belief in its ability to solve its own problems. I found nothing to suggest that, prior to the accident, colliery personnel relied. or were encouraged to rely, "too much" upon state regulation.

Strategic development and physicochemical analysis of oral preparations for unstable drugs

Oral liquid formulations are ideal dosage forms for paediatric, geriatric and patient with dysphagia. Dysphagia is prominent among patients suffering from stroke, motor neurone disease, advanced Alzheimer’s and Parkinson’s disease. However oral liquid preparations are particularly difficult to formulate for hydrophobic and unstable drugs. Therefore current methods employed in solving this issue include the use of ‘specials’ or extemporaneous preparations. In order to challenge this, the government has encouraged research into the field of oral liquid formulations, with the EMEA and MHRA publishing list of drugs of interest. The current work investigates strategic formulation development and characterisation of select API’s (captopril, gliclazide, melatonin, L-arginine and lansoprazole), each with unique obstacles to overcome during solubilisation, stabilisation and when developing a palatable dosage from. By preparing a validated calibration protocol for each of the drug candidates, the oral liquid formulations were assessed for stability, according to the ICH guidelines along with thorough physiochemical characterisation. The results showed that pH and polarity of the solvent had the greatest influence on the extent of drug solubilisation, with inclusion of antioxidants and molecular steric hindrance influencing the extent of drug stability. Captopril, a hydrophilic ACE inhibitor (160 mg.mL-1), undergoes dimerisation with another captopril molecule. It was found that with the addition of EDTA and HP-β-CD, the drug molecule was stabilised and prevented from initiating a thiol induced first order free radical oxidation. The cyclodextrin provided further steric hindrance (1:1 molar ratio) resulting in complete reduction of the intensity of sulphur like smell associated with captopril. Palatability is a crucial factor in patient compliance, particularly when developing a dosage form targeted towards paediatrics. L-arginine is extremely bitter in solution (148.7 g.L-1). The addition of tartaric acid into the 100 mg.mL-1 formulation was sufficient to mask the bitterness associated with its guanidium ions. The hydrophobicity of gliclazide (55 mg.L-1) was strategically challenged using a binary system of a co-solvent and surfactant to reduce the polarity of the medium and ultimately increase the solubility of the drug. A second simpler method was developed using pH modification with L-arginine. Melatonin has two major obstacles in formulation: solubility (100 μg.mL-1) and photosensitivity, which were both overcome by lowering the dielectric constant of the medium and by reversibly binding the drug within the cyclodextrin cup (1:1 ratio). The cyclodextrin acts by preventing UV rays from reaching the drug molecule and initiated the degradation pathway. Lansoprazole is an acid labile drug that could only be delivered orally via a delivery vehicle. In oral liquid preparations this involved nanoparticulate vesicles. The extent of drug loading was found to be influenced by the type of polymer, concentration of polymer, and the molecular weight. All of the formulations achieved relatively long shelf-lives with good preservative efficacy.